Data Assimilation Into Physics-Based Models Via Kalman Filters

Details Data Assimilation Into Physics-Based Models Via Kalman Filters Data Assimilation Into Physics-Based Models Via Kalman Filters

Dec 2002

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2002agufmsa22a..07s&link_type=abstract

American Geophysical Union, Fall Meeting 2002, abstract #SA22A-07

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2427 Ionosphere/Atmosphere Interactions (0335), 2447 Modeling And Forecasting, 2467 Plasma Temperature And Density, 2736 Magnetosphere/Ionosphere Interactions

Scientific paper

The magnetosphere-ionosphere-thermosphere (M-I-T) system is a highly dynamic, coupled, and nonlinear system that can vary significantly from hour to hour at any location. The coupling is particularly strong during geomagnetic storms and substorms, but there are appreciable time delays associated with the transfer of mass, momentum, and energy between the domains. Therefore, both global physics-based models and vast observational data sets are needed to elucidate the dynamics, energetics, and coupling in the M-I-T system. Fortunately, during the coming decade, tens of millions of measurements of the global M-I-T system could become available from a variety of in situ and remote sensing instruments. Some of the measurements will provide direct information about the state variables (densities, drift velocities, and temperatures), while others will provide indirect information, such as optical emissions and magnetic perturbations. The data sources available could include: thousands of ground-based GPS Total Electron Content (TEC) receivers; a world-wide network of ionosondes; hundreds of magnetometers both on the ground and in space; occultations from the COSMIC Satellites, numerous ground-based tomography chains; auroral images from the POLAR Satellite; images of the magnetosphere and plasmasphere from the IMAGE Satellite; SuperDARN radar measurements in the polar regions; the Living With a Star (LWS) Solar Dynamics Observatory and the LWS Radiation Belt and Ionosphere-Thermosphere Storm Probes; and the world-wide network of incoherent scatter radars. To optimize the scientific return and to provide specifications and forecasts for societal applications, the global models and data must be combined in an optimum way. A powerful way of assimilating multiple data types into a time-dependent, physics-based, numerical model is via a Kalman filter. The basic principle of this approach is to combine measurements from multiple instrument types with the information obtained from a physics-based model, taking into account the uncertainties in both the model and measurements. The advantages of this technique and the data sources that might be available will be discussed.

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